Search Results (534)

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article

Terminal forensics in large mobile networks is a vital activity for identifying compromised devices and analyzing malicious actions. In contrast, the study described here begins with the domain of terminal forensics as the primary focus, rather than the threat itself. This paper proposes a new multi-criteria decision-making (MCDM) model that integrates complex picture fuzzy sets (CPFS) with the combinative distance-based assessment (CODAS), referred to throughout as complex picture fuzzy CODAS (CPF-CODAS). The aim is to assist in forensic analysis for detecting mobile botnet command and control (C&C) systems. The CPF-CODAS model accounts for the uncertainty, hesitation, and complex numerical values involved in expert decision-making, using degrees of membership as positive, neutral, and negative values. An illustrative forensic case study is constructed where three mobile devices are evaluated by three cybersecurity professionals based on six key parameters related to botnet activity. The results demonstrate that the model can effectively distinguish suspicious devices and support the use of the CPF-CODAS approach in terminal forensics of mobile networks. The robustness, symmetry, and advantages of this model over existing MCDM methods are confirmed through sensitivity and comparison analyses. In conclusion, this paper introduces a novel probabilistic decision-support tool that digital forensic specialists can incorporate into their workflow to proactively identify and prevent actions of mobile botnet C&C servers. Full article

Background: Pain is a disabling issue in athletes, with significant impact on performance and career longevity. Many randomized clinical trials (RCTs) have explored interventions to reduce pain, leading to multiple systematic reviews with meta-analysis, but their methodological rigor and clinical applicability remain unclear. Objective: To provide an overview of systematic reviews with meta-analysis on interventions aimed at alleviating pain intensity in athletes, identifying knowledge gaps and appraising methodological quality. Methods: CINAHL, Embase, Epistemonikos, PubMed, Scopus, SPORTDiscus, and Cochrane Library were searched from inception to February 2025. Systematic reviews with meta-analysis of RCTs evaluating interventions to manage pain in athletes were considered. Athletes without restrictions in terms of sports, clinical, and sociodemographic characteristics were included. Overlap between reviews was calculated using the corrected covered area. Results: Twelve systematic reviews met inclusion criteria. Physical exercise modalities (e.g., gait retraining, hip strengthening), acupuncture, photo biomodulation, and topical medication showed potential benefits in reducing pain intensity. Other interventions, such as certain manual therapy techniques, platelet-rich plasma, or motor imagery, did not show consistent effects. All reviews focused solely on pain intensity, with minimal stratification by sport or clinical condition which may affect the extrapolation of meta-analyzed findings to the clinical practice. Methodological quality was often low, with flaws in reporting funding sources, lists of excluded studies, and certainty of evidence (was mostly rated as low/very low). Overlap was variable across the interventions. Conclusions: Given low/sparse certainty and minimal sport-specific analyses, no strong clinical recommendations can be made; preliminary signals favor proximal hip strengthening, gait retraining, photo biomodulation (acute soreness), and topical NSAIDs pending higher-quality syntheses. Future reviews should consider mandatory GRADE; pre-registered protocols; sport- and condition-specific analyses; and core outcome sets including multi-dimensional pain. Full article

In the integrated management of water resources, which includes protecting and restoring ecosystems that are directly and indirectly dependent on water, a crucial issue is assessing and identifying areas with the greatest need for improved water retention. This study presents an effective and easy-to-apply method based on the multicriteria decision-making approach, which analyses needs and feasibility. Until now, a point bonitation method has been used to evaluate the need to increase the retention capacity of specific areas. Modification of this method involved applying the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach to estimate the weights of the analysed criteria. The results obtained using the new method were compared with previous studies assessing retention needs in the Masovian Voivodeship (Poland), which relied on the point bonitation method. The final evaluation showed a 74% compliance rate while significantly reducing expert involvement, demonstrating the high applicability of the developed method. Moreover, the DEMATEL method enabled the development of a cause-and-effect model of the criteria and an analysis of their importance. The lowest level of importance (13.6%) was attributed to climatic conditions, while the significance of the remaining criteria (hydrological and hydrogeological conditions, economic use of the catchment area, and catchment area cover) varied within a narrow range, from 20% to 23.5%. Full article

In this study, we established a fully Monte Carlo (MC)-based McCARD/MASTER two-step core design analysis code procedure without relying on conventional deterministic code by incorporating a newly developed MC reflector cross-section generation code. For reflector cross-section generation, the MACAO code was developed and used to produce the discontinuity factors required for whole-core nodal analyses; these factors were generated via the source expansion nodal method. To examine the updated McCARD/MASTER two-step code system, multi-cycle core follow calculations were performed for cycles 1 and 2 of a commercial pressurized water reactor, namely, Hanbit Unit 3. The validity of the nuclear core design parameters, including the critical boron concentration, power distribution, pin power peaking factor, and moderator temperature coefficient, was assessed through comparison with conventional deterministic DeCART2D/MASTER two-step analysis results and the related nuclear design report. Overall, the McCARD/MASTER results were found to be in good agreement, with all the results meeting the design criteria, except for the critical boron concentration at the beginning of cycle 2. To fully exploit the strengths of the MC method, the McCARD few-group constant and reflector cross-section generation system will be extended to heterogeneous nuclear core systems requiring detailed resonance treatment. Furthermore, the newly developed MACAO is expected to facilitate efficient and accurate reflector cross-section generation for the various heterogeneous core systems. Full article

Podocyte injury is a central event in the pathogenesis of diabetic nephropathy (DN). We conducted a systematic review across four major databases, identifying 7769 records and including 130 studies that met predefined eligibility criteria. Methodological quality was assessed with Joanna Briggs Institute tools, yielding a mean score of 81.3%, indicating overall moderate-to-high rigor despite design-contingent limitations. Publication activity was sparse until 2018 but increased markedly thereafter, with more than 80% of studies published between 2019 and 2025. Temporal analyses confirmed a strong positive trend (p = 0.86, p < 0.0001), reflecting the rapid expansion of this field. Study designs evolved from early human-only descriptions to integrated multi-model approaches combining human tissue, animal experiments, and in vitro systems, thus balancing clinical relevance with mechanistic exploration. Geographically, Asia emerged as the leading contributor, complemented by increasing multinational collaborations. Mechanistic synthesis highlighted five reproducible pillars of podocyte injury: slit-diaphragm and adhesion failure, mTOR–autophagy–ER stress disequilibrium, mitochondrial and lipid-driven oxidative injury, immune, complement, and inflammasome activation, and epigenetic and transcriptomic reprogramming. Collectively, these findings underscore a convergent mechanistic cascade driving podocyte dysfunction, while also providing a framework for therapeutic interventions aimed at restoring barrier integrity, metabolic balance, and immune regulation in DN. Full article

The number of IoT devices has been increasing at a rapid rate, and the advent of information-intensive Internet of Multimedia Things (IoMT) applications has placed serious challenges on computing infrastructure, especially for latency, energy efficiency, and responsiveness to tasks. The legacy cloud-centric approach cannot meet such requirements because it suffers from local latency and central resource allocation. To overcome such limitations, fog computing proposes a decentralized model by reducing latency and bringing computation closer to data sources. However, effective scheduling of tasks within heterogeneous and resource-limited fog environments is still an NP-hard problem, especially in multi-criteria optimization and priority-sensitive situations. This research work proposes a new simulated annealing (SA)-based task scheduling framework to perform multi-objective optimization for fog computing environments. The proposed model minimizes makespan, energy consumption, and execution cost, and integrates a priority-aware penalty function to provide high responsiveness to high-priority tasks. The SA algorithm searches the scheduling solution space by accepting potentially sub-optimal configurations during the initial iterations and further improving towards optimality as the temperature decreases. Experimental analyses on benchmark datasets obtained from Google Cloud Job Workloads demonstrate that the proposed approach outperforms ACO, PSO, I-FASC and M2MPA approaches in terms of makespan, energy consumption, execution cost, and reliability at all task volume scales. These results confirm the proposed SA-based scheduler as a scalable and effective solution for smart task scheduling within fog-enabled IoT infrastructures. Full article

This study optimizes the design and fabrication of an injection-molded Alvarez freeform lens using Moldex3D mold flow analysis and CODE V optical design simulations. The dual-software approach facilitates the transition between the manufacturing simulations and the optical design/verification process, thereby addressing the conversion issues between the two analysis modules. The optical quality of the designed lens is evaluated using spot diagram, distortion, and modulation transfer function (MTF) simulations. The Taguchi design methodology is first employed to identify the individual effects of the key injection molding parameters on the quality of the fabricated lens. The quality is then further improved by utilizing two multi-objective optimization methods, namely Gray Relational Analysis (GRA) and Robust Multi-Criteria Optimization (RMCO), to determine the optimal combination of the injection molding parameters. The results demonstrate that RMCO outperforms GRA, showing more substantial improvements in the optical quality of the lens. Overall, the proposed integrated method, incorporating Moldex3D, CODE V, Taguchi robust design, and RMCO analyses, provides an effective approach for optimizing the injection molding of Alvarez freeform lenses, thereby enhancing their quality. Future research could extend this methodology to other optical components and more complex optical systems. Full article

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

The selection of the most economically advantageous tender (MEAT) in public procurement procedures requires transparent evaluation systems capable of integrating heterogeneous criteria, including qualitative ones, to reconcile quality and cost. This systematic review analyzes 74 studies published between 1998 and 2023 to explore the application of multi-criteria decision analysis (MCDA) methods in public construction procurement. The vast majority of MCDA applications focus on the award phase, with constant growth over the last 10 years. However, applications in the prequalification and verification phases are much less frequent and remain under-represented. Geographically, Europe is the most active area in terms of publications, followed by China and some countries in the Asia-Pacific area. In these regions, MCDA has been employed more systematically over time, while in other areas (e.g., Africa, Latin America), applications are sporadic or absent. Analytic Hierarchy Process (AHP) is confirmed as the most widely used technique. Emerging techniques (such as BWM, MABAC, EDAS, VIKOR, advanced TOPSIS) show greater computational rigor and in some cases better theoretical properties, but are less used due to complexity, less practical familiarity and the lack of accessible software tools. The operationalization of environmental and social criteria is still poorly standardized: clear indications on metrics, measurement scales and data sources are often lacking. In most cases, the criteria are treated in a generic or qualitative way, without common standards. Furthermore, the use of sensitivity analyses and procedures for aggregating judgments between evaluators is limited, with a consequent risk of poor robustness and transparency in the evaluation. In order to consider proposing a framework or guidelines based on the review findings, a six-step operational framework that connects selection of criteria and their operationalization, choice of method based on the context, robustness checks and standard minimum reporting, with clear assignment of roles and deliverables, is provided. The framework summarizes and makes the review evidence applicable. Full article

Background: Hypertension remains a leading cause of cardiovascular morbidity and mortality globally, and insulin resistance (IR) and systemic inflammation are implicated in the pathogenesis of hypertension. Limited evidence exists on the predictive role of the triglyceride-glucose (TyG) index and its related indices (TyG-WHtR and TyG-WC) for hypertension. This study aimed to investigate these associations across multiple Chinese cohorts. Methods: Data from 31,224 participants (Fuqing, CHNS, CHARLS) were analyzed. TyG indices were calculated using fasting triglycerides, glucose, and anthropometrics. Hypertension was defined as SBP/DBP ≥ 140/90 mmHg, or physician diagnosis, or antihypertensive treatment. Logistic/Cox regression models were used to examine associations, adjusting for demographics, lifestyle, and metabolic factors. Mediation analysis quantified the role of neutrophil-to-lymphocyte ratio (NLR) in mediating the TyG–hypertension relationship. Results: Elevated TyG index and its obesity-adjusted variants consistently predicted incident hypertension across cohorts (all p < 0.001). Each 1-unit TyG increase was associated with 9–36% higher odds of hypertension in Fuqing (OR = 1.09–1.36). NLR mediated 20.4–29.4% of these associations (p < 0.001). Subgroup analyses revealed effect modifications by age, sex, and residence. Sensitivity analyses confirmed robustness when redefining hypertension thresholds (ACC/AHA criteria). Conclusions: TyG index and its related indices are robust predictors of (new-onset) hypertension, with NLR statistically accounting for approximately 25% of these associations in the mediation model. These findings underscore the interplay between metabolic dysregulation, inflammation, and hypertension and advocate for integrated biomarker strategies in risk stratification and prevention, while external validation in multi-ethnic populations is warranted. Full article

Multi-sensor fusion systems involving Light Detection and Ranging (LiDAR), cameras, and inertial measurement units (IMUs) have been widely adopted in fields such as autonomous driving and robotics due to their complementary perception capabilities. This widespread application has led to a growing demand for accurate sensor calibration. Although numerous calibration methods have been proposed in recent years for various sensor combinations, such as camera–IMU, LiDAR–IMU, camera–LiDAR, and camera–LiDAR–IMU, there remains a lack of systematic reviews and comparative analyses of these approaches. This paper focuses on extrinsic calibration techniques for LiDAR, cameras, and IMU, providing a comprehensive review of the latest developments across the four types of sensor combinations. We further analyze the strengths and limitations of existing methods, summarize the evaluation criteria for calibration, and outline potential future research directions for the benefit of the academic community. Full article

The construction industry faces persistent challenges, including low productivity, high waste generation, and resistance to technological innovation. Off-site modular construction, supported by Building Information Modeling (BIM), emerges as a promising strategy to address these issues and advance sustainability goals. This study aims to evaluate the practical impacts of industrialized off-site construction in the Brazilian context, focusing on cost, execution time, structural weight, and architectural–logistical constraints. The novelty lies in applying the methodology to a high standard, mixed-use multifamily building, an atypical scenario for modular construction in Brazil, and employing a MultiCriteria Decision Analysis (MCDA) to integrate results. A detailed case study is developed comparing conventional and off-site construction approaches using BIM-assisted analyses for weight reduction, cost estimates, and schedule optimization. The results show an 89% reduction in structural weight, a 6% decrease in overall costs, and a 40% reduction in project duration when adopting fully off-site solutions. The integration of results was performed through the Weighted Scoring Method (WSM), a form of MCDA chosen for its transparency and adaptability to case studies. While this study defined weights and scores, the framework allows the future incorporation of stakeholder input. Challenges identified include the need for early design integration, transport limitations, and site-specific constraints. By quantifying benefits and limitations, this study contributes to expanding the understanding of off-site modular adaptability of construction projects beyond low-cost housing, demonstrating its potential for diverse projects and advancing its implementation in emerging markets. Beyond technical and economic outcomes, the study also frames off-site modular construction within the three pillars of sustainability. Environmentally, it reduces structural weight, resource consumption, and on-site waste; economically, it improves cost efficiency and project delivery times; and socially, it offers potential benefits such as safer working conditions, reduced urban disruption, and faster provision of community-oriented buildings. These dimensions highlight its broader contribution to sustainable development in Brazil. Full article

Hispanic populations in the U.S. have a high prevalence of type 2 diabetes and its complications. It has been proposed that interventions targeting multiple levels and domains of influence are needed to address health disparities, but more evidence is needed regarding the most effective approaches. We aimed to review the effects of non-pharmacological interventions on glycemic control among Hispanic persons with diabetes, overall and by level and domain of intervention. A systematic review (PubMed, Scopus, PsycInfo, CINAHL; 1985–2019) identified randomized trials reporting HbA1c outcomes for Hispanic populations. Article review, data extraction, and risk of bias assessment were completed by independent reviewers. Level and domain of intervention were assigned based on the National Institute on Minority Health and Health Disparities Research Framework. Random-effects meta-analyses estimated pooled effect sizes. Quality of evidence was rated based on the GRADE framework. Forty-eight trials met inclusion criteria, representing various Hispanic populations (n = 18 Mexican, n = 5 Puerto Rican, n = 1 Dominican, n = 4 multiple, n = 20 unspecified) and enrolling 9185 total participants. Overall, interventions decreased HbA1c by −0.32% (95% CI: −0.44% to −0.20%, I² = 68%, strength of evidence: moderate). Multi-level, multi-domain interventions decreased HbA1c by −0.41% (−0.61% to −0.21%, I² = 74%, strength of evidence: moderate). Few interventions addressed community (n = 3), society (n = 0), or physical/built environment (n = 1). Non-pharmacological interventions have modestly decreased HbA1c among Hispanic persons with diabetes. Multi-level, multi-domain interventions are promising, but more research is needed on interventions that target social and environmental structures. Full article

Drones have rapidly emerged as transformative tools in landscape research, enabling high-resolution spatial data acquisition, real-time environmental monitoring, and advanced modelling that surpass the limitations of traditional methodologies. This scoping review systematically explores and synthesises the technological applications of drones within the context of landscape studies, addressing a significant gap in the integration of Uncrewed Aerial Systems (UASs) into environmental and spatial planning disciplines. The study investigates the typologies of drone platforms—including fixed-wing, rotary-wing, and hybrid systems—alongside a detailed examination of sensor technologies such as RGB, LiDAR, multispectral, and hyperspectral imaging. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, a comprehensive literature search was conducted across Scopus, Web of Science, and Google Scholar, utilising predefined inclusion and exclusion criteria. The findings reveal that drone technologies are predominantly applied in mapping and modelling, vegetation and biodiversity analysis, water resource management, urban planning, cultural heritage documentation, and sustainable tourism development. Notably, vegetation analysis and water management have shown a remarkable surge in application over the past five years, highlighting global shifts towards sustainability-focused landscape interventions. These applications are critically evaluated in terms of spatial efficiency, operational flexibility, and interdisciplinary relevance. This review concludes that integrating drones with Geographic Information Systems (GISs), artificial intelligence (AI), and remote sensing frameworks substantially enhances analytical capacity, supports climate-resilient landscape planning, and offers novel pathways for multi-scalar environmental research and practice. Full article